On Tue, 2012-03-13 at 12:40 -0400, Paul Gilbert wrote:
> Brian
>> Thanks for spelling this out for those of us that are a bit slow.
> (Newbie questions below)
<... snip ...>
> > So, if your BLAS does multithreaded matrix multiplication, it will use
> > multiple threads 'implicitly', as Simon pointed out.
>> Is there an easy way to know if the R I am using has been compiled with
> multi-thread BLAS support?
BLAS should be 'plug and play', as R is usually compiled to use a shared
object BLAS. As such, installing the BLAS on your machine (and
appropriately configuring it) should 'just work' with te new BLAS when
you restart R.
Dirk et. al. wrote a paper, now a bit dated, that benchmarked some of
the BLAS libraries, that should have some additional details.
<...snip...>
> > Be aware that there can be unintended negative interactions between
> > implicit and explicit parallelization. On cluster nodes I tend to
> > configure the BLAS to use only one thread to avoid resource contention
> > when all cores are doing explicit parallelization.
>> How do you do this? Does it need to be done when you are compiling R, or
> can it be done on the fly while running R processes?
Some BLAS, like gotoblas, support an environment variable to change the
number of cores to be used. This can be changed at run-time. Others,
like the mkl, are always multithreaded. Others, like ATLAS, can be
compiled in either single threaded or multi-threaded modes.
So, for me, on my cluster nodes, I use a single threaded BLAS, assuming
that *explicit* parallelization will be the primary driver of CPU load,
and not wanting to over-commit the processor when 12 calculations each
try to spawn 12 threads in the BLAS. On other machines, I might use a
multithreaded BLAS like gotoblas so that I have some flexibility (though
apparently unlike Claudia, I rarely change it in practice).
Regards,
- Brian
--
Brian G. Peterson
http://braverock.com/brian/
Ph: 773-459-4973
IM: bgpbraverock